12 articles
Function calling lets LLMs request specific tool actions rather than just generating text. Here's how it works, when to use it, and practical examples in Python.
RAG is the most widely used technique in production AI. Here's a clear, jargon-free explanation of how it works, why it matters, and when to use it.
Reasoning models like OpenAI o1/o3 and Claude with extended thinking work differently from standard models. Here's what changes, what doesn't, and how to get the best results.
Context engineering is the practice of designing everything that goes into an AI's context window — not just the prompt. Here's why it matters and how to get better at it.
Learn how Chain of Thought (CoT) prompting forces AI models to reason step-by-step, dramatically improving results for math, logic, and complex reasoning tasks.
Most people never touch system prompts. The ones who do get dramatically better results. Here's what they are, why they matter, and how to write one that actually works.
Two of the most important prompting techniques — and most people don't even realize they're using them. Here's what they actually mean, when each one wins, and how to combine them.
AI has made coding accessible to people who never thought they'd write a line of code. But the gap between 'this doesn't work' and 'this works' is almost entirely in how you prompt. Here's what actually helps.
AI-generated marketing copy has a reputation for being generic and lifeless. That's a prompting problem. Here's how marketers can use AI to create sharper work — without losing what makes a brand distinctive.
Most people use AI to describe their data. Descriptions aren't insights. Here's how to prompt for analysis that actually helps you make decisions.
